Lossy Compression Scheme Research Articles

Statistical mechanics of lossy data compression using a nonmonotonic perceptron.

The performance of a lossy data compression scheme for uniformly biased Boolean messages is investigated via methods of statistical mechanics. Inspired by a formal similarity to the storage capacity problem in neural network research, we utilize a perceptron of which the transfer function is appropriately designed in order to compress and decode the messages. Employing the replica method, we analytically show that our scheme can achieve the optimal performance known in the framework of lossy compression in most cases when the code length becomes infinite. The validity of the obtained results is numerically confirmed.

Fuzzy block truncation coding

Block truncation coding (BTC) is a well known lossy compression scheme. Due to its low complexity and easy implementation, BTC has gained wide interest in its further development and application for image compression. Based on simple thresholding, BTC retains sharp edges and thus leads to artifacts such as the staircase effect. The second problem encountered in BTC is the splitting of homogeneous regions, which produces false contours. In this work a fuzzy approach of BTC to avoid truncating homogeneous blocks and to preserve smooth edges in two-cluster blocks is proposed. Each image block, viewed as a fuzzy set, is segmented into two clusters using a fuzzy clustering algorithm. The block is then encoded by modified fuzzy weighted means of the two clusters. Initialization strategies of the fuzzy clustering algorithm and a contextual quantization method are proposed. Experimental results show an improvement of visual quality of reconstructed images and peak signal-to-noise ratio when compared to BTC, economical BTC (EBTC), absolute moment BTC (AMBTC), and a minimum mean square error quantizer (MMSEQ). Computation time required by AMBTC, EBTC, and fuzzy BTC methods are reported.

Application of bit-plane decomposition steganography to JPEG2000 encoded images

This letter presents a steganography method based on a JPEG2000 lossy compression scheme and bit-plane complexity segmentation (BPCS) steganography. It overcomes the lack of robustness of bit-plane-based steganography methods with respect to lossy compression of a dummy image: a critical shortcoming that has hampered deployment in a practical scenario. The proposed method is based on a seamless integration of the two schemes without compromising their desirable features and makes feasible the deployment of the merits of a BPCS steganography technique in a practical scenario where images are compressed before being transmitted over the network. Embedding rates of around 15% of the compressed image size were achieved for preembedding 1.0-bpp compressed images with no noticeable degradation in image quality.

SPARTAN

While a variety of lossy compression schemes have been developed for certain forms of digital data (e.g., images, audio, video), the area of lossy compression techniques for arbitrary data tables has been left relatively unexplored. Nevertheless, such techniques are clearly motivated by the ever-increasing data collection rates of modern enterprises and the need for effective, guaranteed-quality approximate answers to queries over massive relational data sets.In this paper, we propose SPARTAN, a system that takes advantage of attribute semantics and data-mining models to perform lossy compression of massive data tables. SPARTAN is based on the novel idea of exploiting predictive data correlations and prescribed error-tolerance constraints for individual attributes to construct concise and accurate Classification and Regression Tree (CaRT) models for entire columns of a table. More precisely, SPARTAN selects a certain subset of attributes (referred to as predicted attributes) for which no values are explicitly stored in the compressed table; instead, concise error-constrained CaRTs that predict these values (within the prescribed error tolerances) are maintained. To restrict the huge search space of possible CaRT predictors, SPARTAN uses a Bayesian network structure to guide the selection of CaRT models that minimize the overall storage requirement, based on the prediction and materialization costs for each attribute. SPARTAN 's CaRT-building algorithms employ novel integrated pruning strategies that take advantage of the given error constraints on individual attributes to minimize the computational effort involved. Our experimentation with several real-life data sets offers convincing evidence of the effectiveness of SPARTAN 's model-based approach --- SPARTAN is able to consistently yield substantially better compression ratios than existing semantic or syntactic compression tools (e.g., gzip) while utilizing only small samples of the data for model inference.

Region oriented compression of color images using fuzzy inference and fast merging

A new region based lossy compression scheme for color images is proposed. The segmentation method belongs to the split and merge category. Splitting is carried out using the watershed transform. In the merging stage, a fuzzy color preserving rule-based system and a novel one-dimensional graph structure are introduced to provide accurate results with reduced computational complexity. The compression part is based on the Shape Adaptive DCT with ΔDC correction method. The quantization matrices used have been designed according to the properties of the employed transform. Promising perceptual results for the low bit rate range compared to previously reported compression methods have also been reported.

Adaptive 3D-DCT compression algorithm for continuous parallax 3D integral imaging

Abstract Integral imaging is employed as part of a three-dimensional imaging system, allowing the display of full colour images with continuous parallax within a wide viewing zone. A significant quantity of data is required to represent a captured integral 3D image with high resolution. A lossy compression scheme has been developed based on the use of a 3D-DCT, which make possible efficient storage and transmission of such images, while maintaining all information necessary to produce a high quality 3D display. In this paper, a novel approach to the problem of compressing the significant quantity of data required to represent integral 3D images is presented. The algorithm is based on using a variable number of microlens images (or sub-images) in the computation of the 3D-DCT. It involves segmentation of the planar mean image formed by the mean values of the microlens images and it takes advantage of the high cross-correlation between the sub-images generated by the microlens array. The algorithm has been simulated on several integral 3D images. It was found that the proposed algorithm improves the rate-distortion performance when compared to baseline JPEG and previously reported 3D-DCT compression scheme with respect to compression ratio and subjective and objective image quality.

SPARTAN

While a variety of lossy compression schemes have been developed for certain forms of digital data (e.g., images, audio, video), the area of lossy compression techniques for arbitrary data tables has been left relatively unexplored. Nevertheless, such techniques are clearly motivated by the ever-increasing data collection rates of modern enterprises and the need for effective, guaranteed-quality approximate answers to queries over massive relational data sets. In this paper, we propose SPARTAN , a system that takes advantage of attribute semantics and data-mining models to perform lossy compression of massive data tables. SPARTAN is based on the novel idea of exploiting predictive data correlations and prescribed error tolerances for individual attributes to construct concise and accurate Classification and Regression Tree (CaRT) models for entire columns of a table. More precisely, SPARTAN selects a certain subset of attributes for which no values are explicitly stored in the compressed table; instead, concise CaRTs that predict these values (within the prescribed error bounds) are maintained. To restrict the huge search space and construction cost of possible CaRT predictors, SPARTAN employs sophisticated learning techniques and novel combinatorial optimization algorithms. Our experimentation with several real-life data sets offers convincing evidence of the effectiveness of SPARTAN 's model-based approach — SPARTAN is able to consistently yield substantially better compression ratios than existing semantic or syntactic compression tools (e.g., gzip) while utilizing only small data samples for model inference.

A pyramidal image coder using generalized rank-ordered prediction filter

This paper presents a lossy image compression scheme that employs a generalized rank-ordered prediction filter for pyramidal image coding. The proposed prediction method renders significantly reduced variance of the quantizer input. Consequently, the quality of the decompressed image is much enhanced due to the greatly reduced quantization distortion. Both analytical and simulation results show that the proposed scheme yields high-quality performance.

A review of compression methods for medical images in PACS

As an introduction to Section C2 (medical imaging) of track C (Images and PACS) of MIE ’97, an appropriate and timely topic concerns the coding for the transmission of medical images in PACS. Speed limitations of existing networks along with the explosive growth of image modalities with extremely high volume outputs have combined to make the issue of medical image coding one of the key considerations in the design of future PACS systems. Both lossless and lossy compression schemes are reviewed and compared, and the compression demands are presented of the main digital medical image modalities.

An improved piecewise approximation algorithm for image compression

One-dimensional lossy compression schemes, such as piecewise approximation with triggers (PAT) (Walach and Karnin, IEEE Int. Conf. on Acoustics, Speech and Signal Processing, Vol. 1, pp. 529–532, Tokyo, Japan, April 1986) have the advantage of being computationally simple. Several previous papers have examined this algorithm and a variety of changes have been proposed. This paper adds new features and also incorporates some ideas from previous papers to arrive at a modified version: MPAT. The modifications vary parameters for the trigger function and the thresholds. Furthermore, they also introduce new features such as context modelling, different interpolations, and early triggers. These changes add flexibility, decrease the compressed image size, and improve the reconstruction quality, while maintaining a complexity advantage over other algorithms. A complexity count is also performed to quantitatively demonstrate the benefits of this algorithm over alternatives.

Lossless Image Compression by Block Matching

Lossless image compression has often employed techniques quite separate from those used for text compression or lossy image compression; most standards today employ modelling followed by coding (e.g. the JBIG standard, the IBM Q-coder, CCITT Group 4). Constantinescu and Storer present a lossy image compression scheme that can be viewed as a generalization of lossless dynamic dictionary compression ('LZ2'-type methods) to two dimensions with approximate matching; recently, Constantinescu and Storer have experimented with this approach for lossless bi-level image compression with great success. Here we generalize 'LZ1'-type methods (that identify matches in previously seen text) to lossless image compression. We examine complexity issues and finish by considering practical 2-D implementations for bi-level images.

Fixed-slope universal lossy data compression

Corresponding to any lossless codeword length function l, three universal lossy data compression schemes are presented: one is with a fixed rate, another is with a fixed distortion, and a third is with a fixed slope. The former two universal lossy data compression schemes are the generalization of Yang-Kieffer's (see ibid., vol.42, no.1, p.239-45, 1995) results to the general case of any lossless codeword length function l, whereas the third is new. In the case of fixed-slope /spl lambda/>0, our universal lossy data compression scheme works as follows: for any source sequence x/sup n/ of length n, the encoder first searches for a reproduction sequence y/sup n/ of length n which minimizes a cost function n/sup -1/l(y/sup n/)+/spl lambda//spl rho//sub n/(x/sup n/, y/sup n/) over all reproduction sequences of length n, and then encodes x/sup n/ into the binary codeword of length l(y/sup n/) associated with y/sup n/ via the lossless codeword length function l, where /spl rho//sub n/(x/sup n/, y/sup n/) is the distortion per sample between x/sup n/ and y/sup n/. Under some mild assumptions on the lossless codeword length function l, it is shown that when this fixed-slope data compression scheme is applied to encode a stationary, ergodic source, the resulting encoding rate per sample and the distortion per sample converge with probability one to R/sub /spl lambda// and D/sub /spl lambda//, respectively, where (D/sub /spl lambda//, R/sub /spl lambda//) is the point on the rate distortion curve at which the slope of the rate distortion function is -/spl lambda/. This result holds particularly for the arithmetic codeword length function and Lempel-Ziv codeword length function. The main advantage of this fixed-slope universal lossy data compression scheme over the fixed-rate (fixed-distortion) universal lossy data compression scheme lies in the fact that it converts the encoding problem to a search problem through a trellis and then permits one to use some sequential search algorithms to implement it. Simulation results show that this fixed-slope universal lossy data compression scheme, combined with a suitable search algorithm, is promising.

A suboptimal lossy data compression based on approximate pattern matching

A practical suboptimal (variable source coding) algorithm for lossy data compression is presented. This scheme is based on approximate string matching, and it naturally extends the lossless Lempel-Ziv (1977) data compression scheme. Among others we consider the typical length of an approximately repeated pattern within the first n positions of a stationary mixing sequence where D percent of mismatches is allowed. We prove that there exists a constant r/sub 0/(D) such that the length of such an approximately repeated pattern converges in probability to 1/r/sub 0/(D) log n (pr.) but it almost surely oscillates between 1/r/sub -/spl infin//(D) log n and 2/r/sub 1/(D) log n, where r/sub -/spl infin//(D)>r/sub 0/(D)>r/sub 1/(D)/2 are some constants. These constants are natural generalizations of Renyi entropies to the lossy environment. More importantly, we show that the compression ratio of a lossy data compression scheme based on such an approximate pattern matching is asymptotically equal to r/sub 0/(D). We also establish the asymptotic behavior of the so-called approximate waiting time N/sub l/ which is defined as the time until a pattern of length C repeats approximately for the first time. We prove that log N/sub l//l/spl rarr/r/sub 0/(D) (pr.) as l/spl rarr//spl infin/. In general, r/sub 0/(D)>R(D) where R(D) is the rate distortion function. Thus for stationary mixing sequences we settle in the negative the problem investigated by Steinberg and Gutman by showing that a lossy extension of the Wyner-Ziv (1989) scheme cannot be optimal.

Simple universal lossy data compression schemes derived from the Lempel-Ziv algorithm

Two universal lossy data compression schemes, one with fixed rate and the other with fixed distortion, are presented, based on the well-known Lempel-Ziv algorithm. In the case of fixed rate R, the universal lossy data compression scheme works as follows: first pick a codebook B/sub n/ consisting of all reproduction sequences of length n whose Lempel-Ziv codeword length is /spl les/nR, and then use B/sub n/ to encode the entire source sequence n-block by n-block. This fixed-rate data compression scheme is universal in the sense that for any stationary, ergodic source or for any individual sequence, the sample distortion performance as n/spl rarr//spl infin/ is given almost surely by the distortion rate function. A similar result is shown in the context of fixed distortion lossy source coding.

Lossless compression of RGB color images

Although much work has been done toward developing lossless algorithms for compressing image data, most techniques reported have been for two-tone or gray-scale images. It is generally accepted that a color image can be easily encoded by using a gray-scale compression technique on each of the three (say, RGB) color planes. Such an approach, however, fails to take into account the substantial correlations that are present between color planes. Although several lossy compression schemes that exploit such correlations have been reported in the literature, we are not aware of any such techniques for lossless compression. Because of the difference in goals, the best ways of exploiting redundancies for lossy and lossless compression can be, and usually are, very different. We propose and investigate a few lossless compression schemes for RGB color images. Both prediction schemes and error modeling schemes are presented that exploit interirame correlations. Implementation results on a test set of images yield significant improvements.

Image compression and data integrity in confocal microscopy

AbstractTo manage large volumes of image data generated routinely using real‐time confocal microscopy, compressing image data using a lossy algorithm prior to sustained video rate transferring and/or storing is proposed. Test criteria for determining the acceptability of uncompressed data, both qualitatively and quantitatively, are described, and an empirical demonstration of the use of lossy compression in data management is provided. It is found that, if appropriately used, the lossy compression scheme could retain all the useful information in the data while providing better compression ratios (memory for the original/ memory for the compressed) when compared with a lossless scheme.

Wavelet Projections for Radiosity

One important goal of image synthesis research is to accelerate the process of obtaining realistic images using the radiosity method. Two important concepts recently introduced are the general framework of projection methods and the hierarchical radiosity method. Wavelet theory, which explores the space of hierarchical basis functions, offers an elegant framework that unites these two concepts and allows us to more formally understand the hierarchical radiosity method. Wavelet expansions of the radiosity kernel have negligible entries in regions where high frequency/fine detail information is not needed. A sparse system remains if these entries are ignored. This is similar to applying a lossy compression scheme to the form factor matrix. The sparseness of the system allows for asymptotically faster radiosity algorithms by limiting the number of matrix terms that need to be computed. The application of these methods to 3D environments is described in4. Due to space limitations in that paper many of the subtleties of the construction could not be explored there. In this paper we discuss some of the mathematical details of wavelet projections and investigate the application of these methods to the radiosity kernel of a flatland environment, where many aspect are easier to visualize.

An architecture for lossy compression of waveforms using piecewise-linear approximation

Lossy compression schemes are often desirable in many signal processing applications such as the compression of ECG data. This paper presents a relaxation of a provably good algorithm for lossy signal compression, based on the piecewise linear approximation of functions. The algorithm approximates the data within a given tolerance using a piecewise linear function. The paper also describes an architecture suitable for the single-chip implementation of the proposed algorithm. The design consists of control, two multiply/divide units, four adder/subtracter units, and an I/O interface unit. For uniformly sampled data, no division is required, and all operations can be completed in a pipelined manner in at most three cycles per sample point. The corresponding simplified architecture is also presented. >

Simple method for enhancing the performance of lossy plus lossless image compression schemes

Lossy plus lossless techniques for image compression split an image into a low-bit-rate lossy representation and a residual that represents the difference between this low-rate lossy image and the originaL Conventional schemes encode the lossy image and its lossless residual in an independent manner. We show that making use of the lossy image to encode the residual can lead to significant savings in bit rate. Further, the complexity increase to attain these savings is minimaL The savings are achieved by capturing the inherent structure of the image in the form of a noncausal prediction model that we call a prediction tree. This prediction model is then used to transmit the lossless residuaL Simulation results show that a reduction of 0.5 to 1.0 bit/pixel can be achieved in bit rates compared to the conventional approach of independently encoding the residuaL

Quad tree structures for image compression applications

Traditionally, lossy compression schemes have focused on compressing data at fixed bit rates to either communicate information over limited bandwidth communication channels, or to store information in a fixed-size storage media. In this paper we describe a class of lossy algorithms that is capable of compressing image data over a wide range of rates so that quick browsing of large amounts of information as well as detailed examination of high resolution areas can be achieved by the same compression system. To accomplish this we use a quad tree structure to decompose an image into variable size blocks which are subsequently quantized using a Tree-Structured Vector Quantizer (TSVQ). The developed algorithms utilize variable-size image blocks encoded within quad tree data structures to efficiently encode image areas with different information content. These algorithms are also capable of compressing images so that the loss of information complies with user defined distortion requirements. In this paper we describe the use of quad tree structures in image compression type applications and we analyze their advantages over the classic vector quantization schemes. Finally, we describe their progressive compression capabilities and we demonstrate that they achieve higher compression/ distortion performance compared to the classic TSVQ algorithm.